llama-cpp-python VS intel-extension-for-pytorch

I'll share the core bit that took a while to figure out the right format, my main script is a hot mess using embeddings with SentenceTransformer, so I won't share that yet. E.g: last night I did a PR for llama-cpp-python that shows how Phi might be used with JSON only for the author to write almost exactly the same code at pretty much the same time. https://github.com/abetlen/llama-cpp-python/pull/1184

Python Bindings for llama.cpp

If the model could be made to work with llama.cpp, then https://github.com/abetlen/llama-cpp-python might be more compact. llama.cpp only supports a limited list of model types though.

https://github.com/abetlen/llama-cpp-python has a web server mode that replicates openai's API iirc and the readme shows it has docker builds already.

LocalAI https://localai.io/ and LMStudio https://lmstudio.ai/ both have fairly complete OpenAI compatibility layers. llama-cpp-python has a FastAPI server as well: https://github.com/abetlen/llama-cpp-python/blob/main/llama_... (as of this moment it hasn't merged GGUF update yet though)

I went with Python, llama-cpp-python, since my goal is just to get a small project up and running locally.

I see you’re using gpt4all; do you have a supported way to change the model being used for local inference?

A number of apps that are designed for OpenAI’s completion/chat APIs can simply point to the endpoints served by llama-cpp-python [0], and function in (largely) the same way, while supporting the various models and quants supported by llama.cpp. That would allow folks to run larger models on the hardware of their choice (including Apple Silicon with Metal acceleration) or using other proxies like openrouter.io.

[0]: https://github.com/abetlen/llama-cpp-python

OK I found it. Looks like they use SYCL (which for some reason they've rebranded to DPC++): https://github.com/intel/intel-extension-for-pytorch/tree/v2...

Just to point out it does, kind of: https://github.com/intel/intel-extension-for-pytorch

I've asked before if they'll merge it back into PyTorch main and include it in the CI, not sure if they've done that yet.

Intel already has a working GPGPU stack, using oneAPI/SYCL.

They also have arguably pretty good OpenCL support, as well as downstream support for PyTorch and Tensorflow using their custom extensions https://github.com/intel/intel-extension-for-tensorflow and https://github.com/intel/intel-extension-for-pytorch which are actively developed and just recently brought up-to-date with upstream releases.

https://github.com/intel/intel-extension-for-pytorch :

> Intel® Extension for PyTorch extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*

https://pytorch.org/blog/celebrate-pytorch-2.0/ :

> As part of the PyTorch 2.0 compilation stack, TorchInductor CPU backend optimization brings notable performance improvements via graph compilation over the PyTorch eager mode.

The TorchInductor CPU backend is sped up by leveraging the technologies from the Intel® Extension for PyTorch for Conv/GEMM ops with post-op fusion and weight prepacking, and PyTorch ATen CPU kernels for memory-bound ops with explicit vectorization on top of OpenMP-based thread parallelization*

DLRS Deep Learning Reference Stack: https://intel.github.io/stacks/dlrs/index.html

Install intel extensions for pytorch using docker. https://github.com/intel/intel-extension-for-pytorch

There is: https://github.com/intel/intel-extension-for-pytorch for intel cards on GPUs, but I would assume this doesn't extend to integraded graphics

Nonetheless, this issue might be relevant for your case.

I'm not sure where the disdain for ROCm is coming from, but tensorflow-rocm and the rocm pytorch container were fairly easy to setup and use from scratch once I got the correct Linux kernel installed along with the rest of the necessary ROCm components needed to use tensorflow and pytorch for rocm. TBF Intel Extension for Tensorflow wasn't too bad to setup either (except for the lack of float16 mixed precision training support, that was definitely a pain point to not be able to have), but Intel Extension for Pytorch for Intel GPUs (a.k.a. IPEX-GPU) however, has been a PITA to use for my i5 11400H iGPU NOT because the iGPU itself is slow, BUT because the current i915 driver in the mainline linux kernel simply doesn't work with IPEX-GPU (every script that I've ran ends up freezing when using even the i915 drivers as recent as Kernel version 6), and when I ended up installing drivers that were meant for the Arc GPUs that finally got IPEX-GPUs to work, I ended up with even more issues such as sh*tty FP64 emulation support that basically meant I had to do some really janky workarounds for things to not break while FP64 emulation was enabled (disabling was simply not an option for me, long story short). And yea unlike Intel, both Nvidia AND AMD actually do support FP64 instructions AND FLOAT16 mixed precision training natively on their GPUs so that one doesn't have to worry about running into "unsupported FP64 instructions" and "unsupported training modes" no matter what software they're running on those GPUs.